ML / Data Engineer – Data Science Enablement Job at Amgen (Hyderabad)

Forward Deployed Engineer - Data-as-a-Service

Snorkel AI is hiring data scientists and engineers who will work directly on Sno...

Location

United States , New York City; Redwood City; San Francisco

Salary:

172000.00 - 300000.00 USD / Year

Snorkel AI

Expiration Date

Until further notice

Requirements

2+ years of experience in data science and engineering roles
Strong practical experience with Python, SQL, and data tooling (e.g., pandas, Plotly, Streamlit, Dash)
Familiarity with LLM-based workflows and applying ML techniques in production contexts
Experience leveraging Backend APIs and interpreting associated technical documentation

Job Responsibility

Partner with the Sales Team on client discovery calls to provide technical depth, assess solution fit, and scope Data-as-a-Service opportunities
Develop and present tailored technical assets including specifications, data dictionaries, sample datasets, and client-specific demonstrations to illustrate feasibility and value
Define project scope and success criteria in collaboration with customer stakeholders and internal delivery teams, ensuring alignment on technical requirements and capacity
Design and execute calibration processes including baseline batches, benchmark reports, and evaluation frameworks that establish measurable project success metrics
Build and deploy evaluators, design and implement quality measurement systems to validate project outputs and ensure deliverables meet client expectations
Generate synthetic datasets by developing or adapting existing pipelines to accelerate client engagements and augment training data
Package and deliver production-grade datasets with standardized formatting, comprehensive documentation, and quality assurance
Configure and build custom applications and off-platform solutions for non-standard or specialized client requirements
Define production specifications and workflows, securing technical alignment with client teams to enable seamless go-live transitions
Provide ongoing technical support to Delivery Managers, addressing complex questions, resolving technical blockers, and supporting customer rebuttals

What we offer

Equity in the form of employee stock options

Fulltime

Senior Manager, Data Engineering

You will build a team of talented engineers that will work cross functionally to...

Location

United States , San Jose

Salary:

240840.00 - 307600.00 USD / Year

Archer Aviation

Expiration Date

Until further notice

Requirements

6+ years of experience in a similar role, 2 of which are in a data leadership role
B.S. in a quantitative discipline such as Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or a related field
Expertise with data engineering disciplines including data warehousing, database management, ETL processes, and ML model deployment
Experience with processing and storing telemetry data
Demonstrated experience with data governance standards and practices
3+ years leading teams, including building and recruiting data engineering teams supporting diverse stakeholders
Experience with cloud-based data platforms such as AWS, GCP, or Azure

Job Responsibility

Lead and continue to build a world-class team of engineers by providing technical guidance and mentorship
Design and implement scalable data infrastructure to ingest, process, store, and access multiple data supporting flight test, manufacturing and supply chain, and airline operations
Take ownership of data infrastructure to enable a highly scalable and cost-effective solution serving the needs of various business units
Build and support the development of novel tools to enable insight and decision making with teams across the organization
Evolve data engineering and AI strategy to align with the short and long term priorities of the organization
Help to establish a strong culture of data that is used throughout the company and industry
Lead initiatives to integrate AI capabilities in new and existing tools

Fulltime

ML Engineer

It's an exciting time in our Pricing and Analytics department here at AXA UK. Ou...

Location

United Kingdom , Manchester

Salary:

Not provided

Axa

Expiration Date

Until further notice

Requirements

Expertise leveraging software engineering approaches for statistical analysis and modelling problems with Python, Git
Attitude of continuous improvement
Effective collaboration with technical and non-technical colleagues
Experience applying the above in fast paced, business context
Personal lines insurer or other regulated entity experience a bonus
Eligible and authorised to work in the United Kingdom

Job Responsibility

Enable P&A department to deliver value in a way that is seamless, efficient and controlled, through standardised modelling, analysis, and deployment workflows
Develop Python modelling tools, metrics to assess the value of new models and monitor the value of existing data science models and machine learning services
Raise standards of software engineering approaches and ML processes across the department

What we offer

Competitive annual salary dependent on experience
Annual company & performance-based bonus
Contributory pension scheme (up to 12% employer contributions)
Life Assurance (up to 10 x annual salary)
Private medical cover
25 days annual leave plus Bank Holidays
Opportunity to buy up to 5 extra days leave or sell up to 5 days leave
Wellbeing services & resources
AXA employee discounts

Fulltime

Senior ML Science Manager

Intercom’s AI Group is responsible for defining new ML products, researching app...

Location

Ireland , Dublin

Salary:

Not provided

Intercom

Expiration Date

Until further notice

Requirements

Experience leading, developing and supporting ML teams that deliver impact quickly
A 5+ year track record of your own individual contributions in ML, AI, NLP, or data-related field
Excellent applied ML and coding skills
The ability to thrive in an ambiguous environment with a high degree of autonomy
Strong communication skills, both within technical teams and across disciplines
MSc or PhD in an ML-related field, or similar knowledge

Job Responsibility

Managing, mentoring and coaching a team of 10+ ML scientists, spread across our three offices in Dublin, London and Berlin
Collaborating with ML, engineering, product, and design leaders to shape work of the team and the impact it has on our products
Enabling your team to achieve high levels of productivity, performance and reliability
Expanding the team and promoting its brand both internally and externally (recruitment, event planning, etc.)

What we offer

Competitive salary and equity in a fast-growing start-up
We serve lunch every weekday, plus a variety of snack foods and a fully stocked kitchen
Regular compensation reviews - we reward great work!
Pension scheme & match up to 4%
Peace of mind with life assurance, as well as comprehensive health and dental insurance for you and your dependents
Flexible paid time off policy
Paid maternity leave, as well as 6 weeks paternity leave for fathers, to let you spend valuable time with your loved ones
If you’re cycling, we’ve got you covered on the Cycle-to-Work Scheme. With secure bike storage too
MacBooks are our standard, but we also offer Windows for certain roles when needed

Fulltime

Senior ML Platform Engineer

At WHOOP, we're on a mission to unlock human performance and healthspan. WHOOP e...

Location

United States , Boston

Salary:

150000.00 - 210000.00 USD / Year

Whoop

Expiration Date

Until further notice

Requirements

Bachelor’s or Master’s Degree in Computer Science, Engineering, or a related field
or equivalent practical experience
5+ years of experience in software engineering with a focus on ML infrastructure, cloud platforms, or MLOps
Strong programming skills in Python, with experience in building distributed systems and REST/gRPC APIs
Deep knowledge of cloud-native services and infrastructure-as-code (e.g., AWS CDK, Terraform, CloudFormation)
Hands-on experience with model deployment platforms such as AWS SageMaker, Vertex AI, or Kubernetes-based serving stacks
Proficiency in ML lifecycle tools (MLflow, Weights & Biases, BentoML) and containerization strategies (Docker, Kubernetes)
Understanding of data engineering and ingestion pipelines, with ability to interface with data lakes, feature stores, and streaming systems
Proven ability to work cross-functionally with Data Science, Data Platform, and Software Engineering teams, influencing decisions and driving alignment
Passion for AI and automation to solve real-world problems and improve operational workflows

Job Responsibility

Architect, build, own, and operate scalable ML infrastructure in cloud environments (e.g., AWS), optimizing for speed, observability, cost, and reproducibility
Create, support, and maintain core MLOps infrastructure (e.g., MLflow, feature store, experiment tracking, model registry), ensuring reliability, scalability, and long-term sustainability
Develop, evolve, and operate MLOps platforms and frameworks that standardize model deployment, versioning, drift detection, and lifecycle management at scale
Implement and continuously maintain end-to-end CI/CD pipelines for ML models using orchestration tools (e.g., Prefect, Airflow, Argo Workflows), ensuring robust testing, reproducibility, and traceability
Partner closely with Data Science, Sensor Intelligence, and Data Platform teams to operationalize and support model development, deployment, and monitoring workflows
Build, manage, and maintain both real-time and batch inference infrastructure, supporting diverse use cases from physiological analytics to personalized feedback loops for WHOOP members
Design, implement, and own automated observability tooling (e.g., for model latency, data drift, accuracy degradation), integrating metrics, logging, and alerting with existing platforms
Leverage AI-powered tools and automation to reduce operational overhead, enhance developer productivity, and accelerate model release cycles
Contribute to and maintain internal platform documentation, SDKs, and training materials, enabling self-service capabilities for model deployment and experimentation
Continuously evaluate and integrate emerging technologies and deployment strategies, influencing WHOOP’s roadmap for AI-driven platform efficiency, reliability, and scale

What we offer

equity
benefits

Fulltime

Senior Manager, Data Science and AI Technical Product Owner

Do you want to make a global impact on patient health? Do you thrive in a fast-p...

Location

Mexico; Costa Rica , Mexico City; San Jose

Salary:

Not provided

Pfizer

Expiration Date

Until further notice

Requirements

Bachelor’s degree in analytics related area (Data Science, Computer Engineering, Computer Science, Information Systems, Engineering, or a related discipline)
7+ years of work experience in data science, analytics, engineering, or product management for a diverse range of projects
2-3 years of hands-on product management experience
2+ years of hands-on software development experience, preferably in a cloud environment such as AWS
Track record of managing cross-functional stakeholder groups and effecting change
Clearly articulates expectations, capabilities, and action plans
actively listens with others’ frame of reference in mind
appropriately shares information with team
favorably influences people without direct authority
Clearly articulates scope and deliverables of projects

Job Responsibility

Partner with other leaders to define team roadmap and drive impact by providing strategic and technical input including platform evolution, vendor scan, and new capability development
Communicate value delivered through industrialized AI/ML assets to end user functions (e.g., Chief Marketing Office, PBG Commercial and Medical Affairs) and partner to ideate, design, and implement industrialized assets that can be scaled across markets, brands, and TAs
Partner with Commercial Data team to integrate industrialized assets into enterprise-level analytics data products where appropriate
Partner with Platforms team on continuous development and end to end capability integration between OOB platforms and internal engineered components (API registry, ML library / workflow management, enterprise connectors, AI reusable components)
Performance and resource optimization of managed pipelines and models
Lead the advancement of at scale “industrialized” AI and data science capabilities and industrialized asset products
Own vision and maintain a holistic and consistent roadmap for data science and AI products, including data science workflows, AI based software solutions, and AI components
Provide the voice of the user as the product owner on agile product development teams to maximize value and impact of industrialized data science and AI products
Drive adoption of products through user community engagement (e.g., roadshows, training sessions, knowledge sharing etc.)
Develop and maintain business-facing assets documentation/communication for data science and AI products (e.g., value proposition, catalog, and adoption metrics)

Fulltime

Mts - data engineering

If you've ever watched a massive data pipeline process billions of records witho...

Location

United States , Mountain View

Salary:

139900.00 - 274800.00 USD / Year

Microsoft Corporation

Expiration Date

Until further notice

Requirements

Master's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 4+ years experience in business analytics, data science, software development, data modeling, or data engineering
OR Bachelor's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 6+ years experience in business analytics, data science, software development, data modeling, or data engineering
OR equivalent experience
Experience building and maintaining production data pipelines at scale using technologies such as Apache Spark, Kafka, or similar distributed processing frameworks
Experience writing production-quality Python, Scala, or Java code for data processing applications
Experience building and scaling experimentation frameworks
Experience with cloud data platforms (Azure, AWS, or GCP) and their data services
Experience with schema management and data governance practices
Experience with real-time data processing and streaming architectures
Experience with data orchestration frameworks such as Airflow, Prefect, Dagster or similar workflow management systems

Job Responsibility

Build, maintain, and enhance data ETL pipelines for processing large-scale data with low latency and high throughput to support Copilot operations
Design and maintain high throughput, low latency experimentation reporting pipelines that enable data scientists and product teams to measure model performance and user engagement
Own data quality initiatives including monitoring, alerting, validation, and remediation processes to ensure data integrity across all downstream systems
Implement robust schema management solutions that enable quick and seamless schema evolution without disrupting downstream consumers
Develop and maintain data infrastructure that supports real-time and batch processing requirements for machine learning model training and inference
Collaborate with ML engineers and data scientists to optimize data access patterns and improve pipeline performance for model evaluation workflows
Design scalable data architectures that can handle growing data volumes and evolving business requirements
Implement comprehensive monitoring and observability solutions for data pipelines, including SLA tracking and automated alerting
Partner with cross-functional teams to understand data requirements and translate them into efficient technical solutions

Fulltime

Senior Data Scientist

Join Mentimeter as our Senior Data Scientist, where you will set new standards f...

Location

Sweden , Stockholm

Salary:

Not provided

Mentimeter

Expiration Date

Until further notice

Requirements

A minimum of 8 years of relevant work experience
Proven expertise in data science methodologies, with strong proficiency in machine learning techniques and programming languages (e.g. Python and SQL)
Strong business acumen with a deep understanding of sales, marketing, and product analytics
Expertise in ML modeling with a focus on production deployment and ML engineering best practices (e.g. versioning, monitoring, testing)
A builder mindset and interest to build internal products and systems that scale, leveraging automation, generative AI, and data to drive Mentimeter’s growth and efficiency
Solid software engineering practices, including git, code reviews, and writing performant, maintainable code
Experience collaborating with data engineers on data pipelines, model deployment, and monitoring
Demonstrated ability to deliver high-quality analyses and actionable insights aligned with business objectives
Exceptional communication skills, with the ability to convey complex concepts through clear narratives and visualizations
A track record of mentoring team members and fostering a culture of best practices and continuous improvement

Job Responsibility

Design and deploy machine learning models, such as predictive scoring, clustering, and forecasting, with a strong focus on technical aspects of production deployment
Build and ship internal data products that activate models and insights in the right systems and tools to drive impact
Be a key part of our Analytics team to align priorities, partner with stakeholders and ensure solutions are relevant and impactful
Collaborate closely with our Data Platform Team, Analytics Engineering, and other sister teams to enable the business to proactively act on data and create business impact
Elevate the data science craft within the Data & Analytics department by setting new standards, implementing innovative approaches and best practices
Provide mentorship and technical guidance to team members, fostering a culture of continuous learning and growth
Translate complex data analyses into compelling narratives and visualizations that resonate with diverse audiences

What we offer

A diverse and inclusive work environment
Continuous professional development
Access to a leadership program (including external personal coach)
Relevant education
Competitive compensation and benefits package, including pension contributions

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ML / Data Engineer – Data Science Enablement

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